Scientist, Machine Learning, Molecular Design

Scientist, Machine Learning, Molecular Design

About This Role

The Computational Molecular Science team at Biogen is searching for a scientist interested in machine learning applications and transformative artificial intelligence algorithms to accelerate and improve the established small molecule design cycle that consists of idea generation, evaluation, and synthesis. This is a unique opportunity to join a cross-functional team working at the intersection of computational, chemical, and biological sciences and have direct access to a wealth of structured and unstructured data across the Biogen research organization to make a massive impact on redefining the early drug-discovery platform.

What You’ll Do

• Build and deploy ML-driven molecular design workflows that combine deep generative models and deep reinforcement learning to propose and prioritize small molecules under multi-parameter objectives (potency, ADMET, novelty, synthesizability).
Develop and maintain predictive models and molecular representations (e.g., GNNs/Transformers, 2D/3D features) and integrate them into iterative “generate score
• learn” design loops.
• Collaborate with Computational Chemistry and cross-functional Drug Discovery teams to translate project needs into robust, reproducible tools and deliver measurable impact on small-molecule programs.
• Publish and present methodological and applied work internally and externally.

The successful candidate will be a creative and highly motivated scientist with demonstrated expertise in the field of machine learning and cheminformatics. You will be expected to stay current with the scientific literature and apply the latest advances in computational methods to address drug discovery challenges. As a key contributor to the molecule informatics group, thinking independently to solve complex problems is an important aspect of this position.

Required Skills

• Ph.D. in Computer Science (or related field) with strong fundamentals in machine learning, deep learning, and optimization.
• 1+ years of postdoctoral experience with demonstrated ability to lead research projects independently and deliver high-quality results.
• Proven ability to work effectively in a multidisciplinary environment.
• Strong track record of publications and/or scientific presentations demonstrating impact and scientific rigor.
• Excellent oral and written communication skills, including the ability to explain technical work to both technical and non-technical audiences.

Preferred Skills

• Strong hands-on experience developing and training deep learning models end-to-end (data pipelines, model design, training loops, debugging, and evaluation) using PyTorch or TensorFlow.
• Proven experience with modern deep generative models for molecular design (e.g., Transformer, GAN; diffusion/graph models are a plus)
• Experience applying deep reinforcement learning and/or multi-objective optimization to molecular generation and prioritization (e.g., reward design, constraint handling, Pareto trade-offs).
• Solid understanding of deep learning best practices (regularization, hyperparameter tuning, representation learning, uncertainty estimation, and generalization under data shift).
Strong Python skills and practical experience with the ML ecosystem; cheminformatics experience is a plus.

Job Level: Professional

Additional Information

The base compensation range for this role is: $117,000.00-$152,000.00